Instance shadow detection is a brand new problem, aiming to find shadow
instances paired with object instances. To approach it, we first prepare a new
dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of
shadow and object instances in 1,000 photos, each with individual labeled
masks. Second, we design LISA, named after Light-guided Instance Shadow-object
Association, an end-to-end framework to automatically predict the shadow and
object instances, together with the shadow-object associations and light
direction. Then, we pair up the predicted shadow and object instances, and
match them with the predicted shadow-object associations to generate the final
results. In our evaluations, we formulate a new metric named the shadow-object
average precision to measure the performance of our results. Further, we
conducted various experiments and demonstrate our method's applicability on
light direction estimation and photo editing.

在这篇研究报告中，我们提出了一个全新的问题，即实例阴影检测。为了解决这个问题，我们制作了一个名为 SOBA 的新数据集，设计了一个名为 LISA 的端到端框架来自动预测阴影和对象实例，并配对它们生成最终结果，并且我们还开发了一种新的评估度量标准来衡量我们的结果性能。此外，我们还进行了各种实验，并展示了我们的方法在光线方向估计和照片编辑上的适用性。